Gastos Casa
28 April, 2025
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 17/2/2025 | Comida | 7940 | Andrés | NA |
| 18/2/2025 | Electricidad | 64888 | Andrés | la puse por adelantado para que no se me olvide |
| 18/2/2025 | Comida | 17820 | Tami | Supermercado |
| 23/2/2025 | Comida | 86908 | Tami | Supermercado |
| 27/2/2025 | Comida | 10000 | Andrés | NA |
| 26/2/2025 | Comida | 4620 | Andrés | NA |
| 1/3/2025 | Comida | 2300 | Tami | Supermercado |
| 2/3/2025 | Comida | 102058 | Tami | Supermercado |
| 3/3/2025 | Comida | 9370 | Andrés | NA |
| 9/3/2025 | Comida | 61916 | Tami | Supermercado |
| 11/3/2025 | Comida | 27021 | Andrés | NA |
| 11/3/2025 | Enceres | 13190 | Tami | 40 rollos confort |
| 15/3/2025 | Comida | 78061 | Tami | Supermercado |
| 17/3/2025 | Electricidad | 52458 | Andrés | NA |
| 17/3/2025 | VTR | 22000 | Andrés | NA |
| 21/3/2025 | Agua | 19562 | Andrés | NA |
| 22/3/2025 | Comida | 76766 | Tami | Supermercado |
| 21/3/2025 | Diosi | 18500 | Andrés | antiparasitario |
| 27/3/2025 | Gas | 82450 | Andrés | NA |
| 26/3/2025 | Comida | 4000 | Andrés | avena multigrano y chucrut |
| 29/3/2025 | Comida | 70591 | Tami | Supermercado |
| 3/4/2025 | Gas | 83300 | Andrés | NA |
| 4/4/2025 | Agua | 20807 | Andrés | NA |
| 6/4/2025 | Comida | 52655 | Tami | Supermercado |
| 12/4/2025 | Comida | 72108 | Tami | Supermercado |
| 16/4/2025 | VTR | 21990 | Andrés | NA |
| 22/4/2025 | Comida | 107881 | Tami | Supermercado |
| 26/4/2025 | Comida | 55874 | Tami | Supermercado |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7 # Weekly data
num_periods <- length(tsData_gastos$x) # Total number of periods in your time series
# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)
# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
day = dates,
Actual = as.numeric(tsData_gastos$x),
Seasonal = as.numeric(tsData_gastos$seasonal),
Trend = as.numeric(tsData_gastos$trend),
Random = as.numeric(tsData_gastos$random)
)
tsData_gastos_long <- tsData_gastos_df %>%
pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"),
names_to = "Component", values_to = "Value")
# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
theme(strip.text = element_text(size = 12))
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 1.0071e+09 2 5.156 0.006 **
## lag_depvar 2.6298e+11 1 2692.640 <2e-16 ***
## Residuals 8.1258e+10 832
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 -1841.139 16298.81 0.1476962
## 2-0 31401.979 23239.228 39564.73 0.0000000
## 2-1 24173.141 19447.229 28899.05 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
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## 789 45699.14 2 42564.29
## 790 49553.86 2 45699.14
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## 792 43772.86 2 50018.43
## 793 39235.43 2 43772.86
## 794 39905.00 2 39235.43
## 795 40374.43 2 39905.00
## 796 34230.57 2 40374.43
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## 798 33491.57 2 34324.14
## 799 33366.43 2 33491.57
## 800 46646.86 2 33366.43
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## 802 57339.86 2 49770.86
## 803 59799.14 2 57339.86
## 804 53577.14 2 59799.14
## 805 61775.29 2 53577.14
## 806 70627.86 2 61775.29
## 807 57888.43 2 70627.86
## 808 49960.71 2 57888.43
## 809 42923.71 2 49960.71
## 810 47284.86 2 42923.71
## 811 52284.86 2 47284.86
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## 815 43199.14 2 34525.14
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## 817 43200.86 2 52757.43
## 818 36772.29 2 43200.86
## 819 29568.00 2 36772.29
## 820 42362.00 2 29568.00
## 821 42566.29 2 42362.00
## 822 39596.00 2 42566.29
## 823 32925.00 2 39596.00
## 824 43416.57 2 32925.00
## 825 52624.86 2 43416.57
## 826 57733.71 2 52624.86
## 827 54120.57 2 57733.71
## 828 53353.43 2 54120.57
## 829 56286.86 2 53353.43
## 830 60626.86 2 56286.86
## 831 61375.29 2 60626.86
## 832 53710.86 2 61375.29
## 833 55795.57 2 53710.86
## 834 55130.14 2 55795.57
## 835 57700.14 2 55130.14
## 836 61333.14 2 57700.14
## 837 59230.71 2 61333.14
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 680 53636.24 22138.804
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86 50363.14 62270.86
## [617] 67061.57 59609.00 85054.00 68023.29 59242.29 61535.14 56215.86
## [624] 45152.29 57409.57 35151.43 34991.43 45944.71 57944.71 55706.29
## [631] 88593.71 77359.43 79878.71 81753.00 75716.00 67381.43 63528.57
## [638] 49682.86 47815.00 46546.14 44808.71 42959.57 46023.86 51309.57
## [645] 68447.29 84959.29 81666.29 82700.86 89422.14 104812.71 98812.71
## [652] 64779.86 61862.86 58376.43 59503.57 55429.43 44454.57 47184.00
## [659] 52126.71 51202.00 64437.14 64297.14 64628.57 51413.14 52969.43
## [666] 54135.29 48799.43 41907.86 45382.00 42633.29 46624.71 44051.86
## [673] 35852.86 29737.71 29734.86 32881.71 38298.57 40886.14 38601.86
## [680] 38628.86 39142.57 32666.14 39911.57 39336.29 39678.86 41963.14
## [687] 54220.57 63901.86 73116.00 60863.86 56293.86 52725.00 58625.00
## [694] 47513.00 40300.14 33312.43 29556.71 27816.71 34120.29 32132.57
## [701] 32902.57 39694.14 72501.29 79551.14 99637.71 95424.29 98395.14
## [708] 115594.71 114267.57 88353.29 88750.86 78835.71 75519.14 73202.86
## [715] 53433.29 48165.71 52163.14 49306.86 36846.86 43220.57 38952.29
## [722] 41522.29 39090.00 28452.57 32975.00 33690.71 26405.29 47087.43
## [729] 49660.29 47409.71 53881.71 45189.57 45503.86 54640.14 39131.29
## [736] 35024.14 44755.43 41063.29 42783.29 45952.57 44937.43 40838.43
## [743] 48838.43 43139.14 67134.29 73224.29 68770.71 59539.29 82179.86
## [750] 74252.14 73015.00 56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57 61025.43 43913.86 46099.29 44524.86
## [764] 42208.71 166486.57 171565.29 200415.71 204498.14 197558.86 195266.57
## [771] 203144.29 85493.71 74721.57 36232.14 40161.71 40629.86 45663.71
## [778] 39252.29 39618.57 39438.43 44650.71 38626.71 38280.43 44134.14
## [785] 47596.43 45598.43 42564.29 45699.14 49553.86 50018.43 43772.86
## [792] 39235.43 39905.00 40374.43 34230.57 34324.14 33491.57 33366.43
## [799] 46646.86 49770.86 57339.86 59799.14 53577.14 61775.29 70627.86
## [806] 57888.43 49960.71 42923.71 47284.86 52284.86 50191.00 36465.86
## [813] 34525.14 43199.14 52757.43 43200.86 36772.29 29568.00 42362.00
## [820] 42566.29 39596.00 32925.00 43416.57 52624.86 57733.71 54120.57
## [827] 53353.43 56286.86 60626.86 61375.29 53710.86 55795.57 55130.14
## [834] 57700.14 61333.14 59230.71
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [778] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [815] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6 7
## 2020.18714 4040.92944 -538.59030 2437.62214 -2970.65691 518.37343
## 8 9 10 11 12 13
## -5656.39621 -1187.07903 -3965.32977 -416.41413 -4938.37141 -1607.13473
## 14 15 16 17 18 19
## -897.47542 379.59513 -3241.19288 -375.72724 -2128.19013 6606.24254
## 20 21 22 23 24 25
## -1529.23255 -1208.09507 1475.94601 -1186.82334 234.61443 1694.79763
## 26 27 28 29 30 31
## -7102.74879 948.68553 8193.15982 416.90084 -15.21894 -2401.64173
## 32 33 34 35 36 37
## 1575.86976 4571.83542 1125.23596 2389.73408 -1870.04340 4606.63268
## 38 39 40 41 42 43
## 4302.99375 -2277.11025 -2982.07617 -1109.95282 -10741.00264 7292.39215
## 44 45 46 47 48 49
## 2558.07618 1366.91759 8104.88529 683.91106 6526.64573 6710.95292
## 50 51 52 53 54 55
## -5886.97774 -4798.01231 -5060.76465 -7928.19669 6132.11686 -4076.13050
## 56 57 58 59 60 61
## -4892.90263 3858.25094 888.97789 -31.36286 142.86855 -4995.92584
## 62 63 64 65 66 67
## 18128.45340 3637.60218 -3649.66056 5923.22432 7340.78161 14634.32937
## 68 69 70 71 72 73
## 1685.74245 -13219.49701 -1308.56105 4641.86019 -4902.79487 -4405.35650
## 74 75 76 77 78 79
## -10496.88629 2470.36700 -5397.11875 1067.72707 -6862.93168 551.88162
## 80 81 82 83 84 85
## -2350.24200 -2689.28064 -3926.96841 -531.97306 2319.74048 3766.56214
## 86 87 88 89 90 91
## 479.20935 -482.70994 198.31551 4303.33719 -1163.04613 1151.13649
## 92 93 94 95 96 97
## -2064.55884 -1043.78236 178.33034 275.38427 -7483.59403 2394.41106
## 98 99 100 101 102 103
## -8600.80050 -2936.33526 -4034.60869 -1730.96353 -1255.47932 3186.71134
## 104 105 106 107 108 109
## -2337.87118 2598.48007 -1155.24356 973.63158 2589.60842 -3152.95841
## 110 111 112 113 114 115
## -4720.79506 -846.76060 1906.93093 11696.00409 -1244.00768 2667.70331
## 116 117 118 119 120 121
## 4261.36253 3500.16963 -1103.10397 -4718.65729 -3724.58067 2320.59970
## 122 123 124 125 126 127
## -1732.52664 1341.16602 8858.58503 844.79807 128.27281 -2523.11262
## 128 129 130 131 132 133
## 2654.31442 7051.14839 1009.18781 -8502.47215 1749.16421 4135.00738
## 134 135 136 137 138 139
## -3165.59747 -1420.15005 -853.84797 -3879.56661 1184.69078 -494.33135
## 140 141 142 143 144 145
## -2912.38767 1720.18184 -1879.78976 -7827.54910 2043.44546 -3476.82175
## 146 147 148 149 150 151
## 2105.83734 -254.98433 1025.26268 -357.68719 1353.69489 1187.50647
## 152 153 154 155 156 157
## 3356.95771 -4862.50427 -1173.58717 -3234.66009 5958.74011 9746.57115
## 158 159 160 161 162 163
## -3661.49075 -5007.12181 3376.42363 -34.52913 2466.31776 -6141.98510
## 164 165 166 167 168 169
## -6977.10804 3929.03574 17162.01374 3382.04614 -646.87695 -2693.70822
## 170 171 172 173 174 175
## -1349.87331 3345.58385 -475.80346 -8322.72706 2622.05685 4081.09640
## 176 177 178 179 180 181
## 377.59095 8501.94928 -9503.56790 -3719.28745 -10989.79661 -11477.93593
## 182 183 184 185 186 187
## 1003.93760 9059.22890 -1673.18391 5685.42216 6305.42015 12900.47414
## 188 189 190 191 192 193
## 8158.27201 -4345.69874 2184.92595 10084.94460 -1939.00724 -2737.93338
## 194 195 196 197 198 199
## -10570.85544 -6641.92953 962.03719 -5505.99275 -10062.55580 5128.03097
## 200 201 202 203 204 205
## -3329.81124 -1970.54715 -1060.71985 6237.92858 9615.44502 296.75843
## 206 207 208 209 210 211
## 2641.80935 2811.37153 5494.41691 12537.47881 -5997.86916 -11596.10220
## 212 213 214 215 216 217
## -5949.32316 -10861.53428 -5334.91080 1273.52302 -13266.19516 16149.75446
## 218 219 220 221 222 223
## 7546.09629 1253.23911 26410.92654 12211.22734 7004.76207 13690.30455
## 224 225 226 227 228 229
## -4264.41963 -2079.91390 3446.77050 30.23710 2422.46429 8684.05578
## 230 231 232 233 234 235
## 5505.84990 -2229.11624 -2141.02196 9120.82933 -11820.85268 -7576.46650
## 236 237 238 239 240 241
## -8822.37175 -10370.50740 2821.63624 1091.44600 -8559.75194 -9241.57243
## 242 243 244 245 246 247
## 8853.30472 -8021.17563 2240.74425 -10553.08222 -4293.64849 1185.56773
## 248 249 250 251 252 253
## 760.61606 -12562.71900 3410.85935 1820.73519 3963.55752 1877.31836
## 254 255 256 257 258 259
## -1423.51721 10875.92971 20598.11105 2878.48679 -4593.88036 3795.83496
## 260 261 262 263 264 265
## -2013.04703 3423.90329 -5168.83653 -11200.27257 -5015.90452 -801.11571
## 266 267 268 269 270 271
## -5467.36649 8506.57468 -4569.23441 3906.82280 -2398.44922 4142.13045
## 272 273 274 275 276 277
## 411.13465 7003.22218 -1726.03535 11714.03468 -4919.28141 1400.07792
## 278 279 280 281 282 283
## -699.94007 7526.00165 -5397.25009 -3057.32102 -11578.78878 -2959.97690
## 284 285 286 287 288 289
## 18370.40629 7458.02975 2393.25749 -972.59289 566.68482 6059.87930
## 290 291 292 293 294 295
## 6532.60152 -19133.75091 -11449.05531 -8400.73650 9407.05004 2790.32367
## 296 297 298 299 300 301
## -1467.78169 27116.95776 9711.61037 4526.83300 9138.76374 2461.32373
## 302 303 304 305 306 307
## -1425.13800 7516.24129 -24686.72394 -3850.06704 -475.45063 -7263.59856
## 308 309 310 311 312 313
## -4244.35867 2672.77823 -9458.51209 -3468.96300 -8415.83738 1358.27827
## 314 315 316 317 318 319
## -3365.75586 1839.40944 -4301.71971 27233.52879 -1040.09014 2978.65513
## 320 321 322 323 324 325
## 10509.77483 5239.53688 32020.43530 4665.77363 -21378.88051 1429.83301
## 326 327 328 329 330 331
## 752.30372 -6817.13902 -2058.13079 -33579.70784 711.93710 -2474.78518
## 332 333 334 335 336 337
## -258.87275 -3334.82876 3926.46344 -612.87076 -7129.43478 -3273.31611
## 338 339 340 341 342 343
## -2342.24209 -7827.55937 3723.70351 -1519.62629 -1887.59273 -1143.63278
## 344 345 346 347 348 349
## 24.43877 323.09901 -1784.32898 -9612.40362 -13350.02592 2206.39263
## 350 351 352 353 354 355
## -4445.51501 -3775.08706 -6093.45148 1647.76107 1263.62102 2616.66324
## 356 357 358 359 360 361
## -3922.92845 -668.06069 518.84932 6844.74402 75.07006 -245.01140
## 362 363 364 365 366 367
## 2372.90850 -2972.68715 -1090.36024 -8953.98016 -4803.47927 -6374.89107
## 368 369 370 371 372 373
## -5092.27125 -7382.39329 4905.19429 230.62519 6969.20702 -7822.37772
## 374 375 376 377 378 379
## -2423.00651 -3544.24877 -2615.47231 -12602.23441 1799.66834 -10755.53618
## 380 381 382 383 384 385
## 5606.39700 9210.63478 2952.49206 -2591.81895 1415.92053 6543.52274
## 386 387 388 389 390 391
## 11177.93639 -6085.07056 -5624.56392 -399.77571 8319.55481 1534.34709
## 392 393 394 395 396 397
## 10934.20217 -10211.51257 2483.59353 412.11975 261.43908 -954.23005
## 398 399 400 401 402 403
## -857.86214 -14776.91060 8301.65949 -1433.87201 -1617.06978 6745.37615
## 404 405 406 407 408 409
## -8197.23973 -1523.41511 -2749.68891 -6023.84152 -3036.82060 -4083.19142
## 410 411 412 413 414 415
## -8906.64771 6015.65188 1487.67929 -7539.58689 -7831.25136 14108.45470
## 416 417 418 419 420 421
## 3628.42203 4279.23519 -8273.77621 -4946.95498 -2783.54318 2647.07107
## 422 423 424 425 426 427
## -14199.34344 -2920.77683 -9222.23151 2921.93234 6861.66771 6418.53392
## 428 429 430 431 432 433
## -4181.04568 -4300.05806 -4886.79696 -1937.93219 -5858.18906 -6754.36974
## 434 435 436 437 438 439
## -6056.61080 -1484.52039 -944.52047 -5079.42047 2488.00923 4722.59601
## 440 441 442 443 444 445
## -5204.93367 -2291.69156 1444.22295 -3984.21155 2698.27584 -6735.19197
## 446 447 448 449 450 451
## -12245.05633 -4602.18243 9562.41791 -2166.69840 4621.40545 -6029.29658
## 452 453 454 455 456 457
## -1262.21050 243.03889 2878.01268 -12434.55318 3249.42564 -6842.55833
## 458 459 460 461 462 463
## 6402.27496 2857.83208 2335.42131 -4030.90662 1921.47292 -190.17600
## 464 465 466 467 468 469
## 1608.15450 -715.20698 3157.53075 -2846.78906 5609.30813 -7161.29139
## 470 471 472 473 474 475
## -3151.66395 -2379.54642 -4829.16664 2848.76635 7634.09632 -6214.29362
## 476 477 478 479 480 481
## 1312.38092 -6357.72165 -2998.74954 1866.49045 -13086.94234 -9864.70052
## 482 483 484 485 486 487
## -1281.89055 -65.34161 -1058.12764 -1443.39409 -9689.90924 11018.17796
## 488 489 490 491 492 493
## 6104.02596 7258.60281 -5630.59334 5194.37131 9097.04937 5821.49383
## 494 495 496 497 498 499
## -13726.20912 -10759.65156 -3592.13393 -1245.77278 -663.47894 -7766.74366
## 500 501 502 503 504 505
## 496.54540 4165.13435 5364.32579 491.29767 -92.28228 -7412.91955
## 506 507 508 509 510 511
## 423.40858 -5199.90368 1697.83558 -1442.19409 -8301.80283 -718.47844
## 512 513 514 515 516 517
## -2794.61084 -703.48762 1212.42546 -9625.95405 -7865.39752 24207.12835
## 518 519 520 521 522 523
## 9643.64831 5659.32653 -5575.64515 2583.15736 16795.48746 11187.57226
## 524 525 526 527 528 529
## -24470.52821 -5276.65957 -3927.55106 4393.32832 -553.72451 -11297.44216
## 530 531 532 533 534 535
## 4240.11994 13738.53128 -5202.86300 4169.49663 5334.72890 -2029.84783
## 536 537 538 539 540 541
## -4769.19243 -7280.92003 -2277.55050 8153.73970 -74.38152 -8341.77886
## 542 543 544 545 546 547
## 1648.70643 -774.82983 192.90415 -11206.24553 -11195.31596 1944.78796
## 548 549 550 551 552 553
## 6891.80436 -1462.59817 693.42695 -7871.01223 8440.98707 744.62352
## 554 555 556 557 558 559
## -12112.01991 9037.84422 8491.71545 -103.26732 4650.78158 -3794.87148
## 560 561 562 563 564 565
## 13903.83047 21240.28625 -6802.89379 -9979.51526 6523.44870 -45.81316
## 566 567 568 569 570 571
## 3189.45297 -7648.39066 -17541.71835 6502.99203 6249.76329 1694.78436
## 572 573 574 575 576 577
## 2887.24036 1551.07092 -2385.96576 14509.05068 -9910.58722 -6462.76176
## 578 579 580 581 582 583
## 8520.33800 2635.55307 -6775.12770 7309.04504 -4025.85111 -2982.39754
## 584 585 586 587 588 589
## 15509.53543 -14750.23522 8238.08587 -149.92571 -6428.29757 -941.87715
## 590 591 592 593 594 595
## 69.41437 -10834.94703 1656.92587 -7293.15096 2944.29859 8727.60987
## 596 597 598 599 600 601
## -7675.47271 5704.74549 2563.96948 6674.35822 -3396.36145 5958.54272
## 602 603 604 605 606 607
## -8510.15621 2078.76483 1086.27101 2951.72329 1299.21961 199.09064
## 608 609 610 611 612 613
## -6006.80618 7903.36412 -1383.52495 -2765.37792 -3631.42697 -8391.12479
## 614 615 616 617 618 619
## 11826.87863 4759.38261 -9507.24298 11470.85387 5852.92424 -5787.25514
## 620 621 622 623 624 625
## 26172.10692 -13100.31874 -6994.61284 2973.79815 -4349.69534 -10763.62960
## 626 627 628 629 630 631
## 11164.42689 -21807.92352 -2511.87363 8581.26957 11006.90056 -1720.83973
## 632 633 634 635 636 637
## 33123.22010 -6858.27310 5481.00635 5153.16096 -2522.16963 -5579.74315
## 638 639 640 641 642 643
## -2147.27382 -12625.16981 -2390.35920 -2026.50502 -2654.81375 -2985.25412
## 644 645 646 647 648 649
## 1695.38458 4302.57819 16820.00041 18351.76481 625.47190 4538.48528
## 650 651 652 653 654 655
## 10355.44248 19870.87549 417.83373 -28370.36756 -1538.93030 -2475.58202
## 656 657 658 659 660 661
## 1699.08051 -3360.30841 -10773.91935 1548.73402 4105.62938 -1139.55748
## 662 663 664 665 666 667
## 12903.88674 1194.92516 1648.72904 -11856.40434 1251.61052 1057.10383
## 668 669 670 671 672 673
## -5297.83989 -7525.28892 1972.84068 -3812.65422 2581.45110 -3480.35091
## 674 675 676 677 678 679
## -9430.39253 -8378.71316 -3036.26700 113.08759 2779.24761 631.89378
## 680 681 682 683 684 685
## -3914.21221 -1890.49680 -1400.38346 -8325.85447 4580.68060 -2327.90168
## 686 687 688 689 690 691
## -1482.46765 502.37319 10763.08635 9730.03964 10481.68053 -9824.63038
## 692 693 694 695 696 697
## -3684.91825 -3259.09584 5760.47541 -10508.76952 -8008.52401 -8691.41272
## 698 699 700 701 702 703
## -6339.10090 -4796.19608 3028.32555 -4469.39920 -1961.91964 4156.58762
## 704 705 706 707 708 709
## 31027.15466 9399.98282 23324.20849 1552.92083 8206.77511 22809.49266
## 710 711 712 713 714 715
## 6448.04429 -18306.17349 4743.31633 -5519.34742 -169.00017 413.76008
## 716 717 718 719 720 721
## -17331.12444 -5317.92944 3283.93235 -3066.54289 -13029.83696 4235.27933
## 722 723 724 725 726 727
## -5604.32938 696.61890 -3982.12774 -12493.47273 1327.23119 -1910.15144
## 728 729 730 731 732 733
## -9821.19253 17229.21126 1723.61479 -2775.91502 5663.33042 -8686.04789
## 734 735 736 737 738 739
## -773.87918 8087.68646 -15407.28308 -5957.98953 7363.38800 -4834.96230
## 740 741 742 743 744 745
## 112.37415 1778.19185 -2007.25316 -5218.90734 6364.06672 -6328.09347
## 746 747 748 749 750 751
## 22648.84809 7764.47034 -2012.42679 -7350.94711 23358.90197 -4359.14657
## 752 753 754 755 756 757
## 1333.39943 -14483.77391 56055.99612 26848.31152 15020.94788 -10717.33774
## 758 759 760 761 762 763
## 10546.77351 7255.69568 5752.82127 -46443.45586 -16206.14934 936.66312
## 764 765 766 767 768 769
## -2548.06891 -3487.98909 122814.43008 19260.71247 43671.78961 22535.78998
## 770 771 772 773 774 775
## 12028.01545 15801.42398 25682.84655 -98853.70828 -6786.39138 -35859.78960
## 776 777 778 779 780 781
## 1713.74966 -1252.98245 3371.66667 -7439.90330 -1469.32818 -1969.64479
## 782 783 784 785 786 787
## 3400.10547 -7180.00199 -2260.65323 3895.75262 2241.25219 -2783.16399
## 788 789 790 791 792 793
## -4070.83645 1716.19321 2830.69969 -74.17053 -6725.82817 -5803.94461
## 794 795 796 797 798 799
## -1168.16462 -1284.01467 -7838.20370 -2374.22955 -3288.59263 -2685.97705
## 800 801 802 803 804 805
## 10703.84006 2219.29381 7057.57633 2900.72868 -5470.95586 8165.89512
## 806 807 808 809 810 811
## 9852.39356 -10625.15011 -7417.21129 -7524.52243 2987.72764 4175.61207
## 812 813 814 815 816 817
## -2288.79162 -14183.67446 -4127.11360 6243.28283 8219.54439 -9692.01356
## 818 819 820 821 822 823
## -7767.09694 -9352.10852 9739.22469 -1239.84410 -4388.69786 -8463.34347
## 824 825 826 827 828 829
## 7859.41117 7896.91662 4956.72550 -3122.11695 -730.97800 2873.01729
## 830 831 832 833 834 835
## 4648.88006 1603.67423 -6714.96272 2069.29993 -418.39680 2733.26050
## 836 837
## 4119.79958 -1158.26811
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17249.10 20098.07 24354.73 24072.52 26427.37 23758.34 24475.11 19704.22
## 10 11 12 13 14 15 16 17
## 19440.62 16781.70 17559.66 14286.99 14338.19 15003.26 16700.91 15019.87
## 18 19 20 21 22 23 24 25
## 16055.19 15428.33 22515.23 21598.67 21078.20 22969.39 22294.96 22947.92
## 26 27 28 29 30 31 32 33
## 24795.03 18719.60 20446.84 28289.10 28346.79 28019.50 25647.42 27050.74
## 34 35 36 37 38 39 40 41
## 30896.19 31244.84 32654.90 30163.94 34140.01 37350.11 34404.36 31213.24
## 42 43 44 45 46 47 48 49
## 30060.29 20633.89 28157.35 30595.37 31685.26 38527.66 38021.93 42687.05
## 50 51 52 53 54 55 56 57
## 46925.98 39619.30 34184.34 29203.91 22344.03 28637.99 25216.47 21511.75
## 58 59 60 61 62 63 64 65
## 25922.88 27183.22 27480.42 27892.50 23760.83 40362.54 42207.66 37450.63
## 66 67 68 69 70 71 72 73
## 41660.22 46578.96 57253.83 55266.35 40500.28 38004.57 41024.37 35320.93
## 74 75 76 77 78 79 80 81
## 30770.31 21467.92 24671.40 20594.56 22681.93 17574.26 19590.96 18816.99
## 82 83 84 85 86 87 88 89
## 17844.11 15911.83 17190.40 20800.72 25221.22 26211.71 26236.68 26853.81
## 90 91 92 93 94 95 96 97
## 30981.47 29811.29 30811.27 28874.50 28073.81 28442.19 28849.02 22422.45
## 98 99 100 101 102 103 104 105
## 25439.37 18465.48 17320.89 15360.39 15660.34 16338.15 20813.59 19896.52
## 106 107 108 109 110 111 112 113
## 23409.81 23199.65 24876.82 27755.39 25251.94 21693.19 21968.78 24616.71
## 114 115 116 117 118 119 120 121
## 35488.01 33679.73 35518.35 38518.54 40475.68 38162.66 32980.44 29319.54
## 122 123 124 125 126 127 128 129
## 31403.67 29682.55 30864.84 38469.34 38111.58 37172.54 34034.11 35816.42
## 130 131 132 133 134 135 136 137
## 41217.67 40657.62 31853.84 33119.42 36311.17 32719.58 31105.85 30190.28
## 138 139 140 141 142 143 144 145
## 26745.17 28160.47 27929.96 25614.82 27640.50 26264.41 19862.55 22894.96
## 146 147 148 149 150 151 152 153
## 20720.31 23699.27 24239.59 25830.97 26013.16 27668.35 28969.90 32003.93
## 154 155 156 157 158 159 160 161
## 27471.30 26733.80 24287.55 30185.29 41681.92 40011.12 37374.43 42397.81
## 162 163 164 165 166 167 168 169
## 43807.25 47225.27 42688.39 37992.68 43421.27 59733.53 61947.02 60360.14
## 170 171 172 173 174 175 176 177
## 57183.87 55582.13 58286.37 57309.87 49597.23 52422.48 56167.41 56203.62
## 178 179 180 181 182 183 184 185
## 63336.85 53833.29 50582.23 41385.22 32919.35 36429.77 46539.47 45995.15
## 186 187 188 189 190 191 192 193
## 51951.58 57700.10 68489.73 73775.84 67466.65 67660.20 74734.86 70408.65
## 194 195 196 197 198 199 200 201
## 65928.71 55165.93 49192.39 50617.56 46209.56 38373.54 44802.24 43028.55
## 202 203 204 205 206 207 208 209
## 42666.29 43144.93 49943.13 58837.81 58467.19 60193.06 61849.87 65643.38
## 210 211 212 213 214 215 216 217
## 75115.73 67193.67 55375.47 49980.96 40971.77 37927.62 41043.20 31057.25
## 218 219 220 221 222 223 224 225
## 48041.19 55366.48 56268.93 79048.34 86547.95 88552.41 96148.42 87093.77
## 226 227 228 229 230 231 232 233
## 81088.52 80670.19 77318.11 76479.09 81219.01 82584.12 77016.16 72226.17
## 234 235 236 237 238 239 240 241
## 77883.28 64522.90 56554.51 48500.22 40106.65 44301.13 46455.18 39901.86
## 242 243 244 245 246 247 248 249
## 33577.55 43866.32 38109.68 42047.80 34306.93 33012.00 36669.53 39495.15
## 250 251 252 253 254 255 256 257
## 30319.00 36260.69 40064.44 45262.40 47982.37 47474.64 57781.89 75289.80
## 258 259 260 261 262 263 264 265
## 75104.74 68411.31 69894.05 66112.53 67559.55 61313.42 50581.48 46606.40
## 266 267 268 269 270 271 272 273
## 46815.94 42920.28 51729.81 48000.61 52149.88 50265.30 54335.15 54631.35
## 274 275 276 277 278 279 280 281
## 60652.46 58285.25 67964.14 61885.21 62095.37 60443.43 66189.82 59916.46
## 282 283 284 285 286 287 288 289
## 56478.22 46024.12 44419.88 61662.68 67196.17 67605.88 65021.89 64108.69
## 290 291 292 293 294 295 296 297
## 68112.11 72024.75 53009.63 43105.59 37112.95 47440.68 50684.50 49797.90
## 298 299 300 301 302 303 304 305
## 74009.10 79958.17 80626.24 85241.53 83439.00 78466.19 81935.15 56818.50
## 306 307 308 309 310 311 312 313
## 53077.31 52756.88 46543.22 43750.94 47356.51 39904.11 38625.41 33183.58
## 314 315 316 317 318 319 320 321
## 36970.47 36151.30 39985.15 37968.33 63770.66 61610.49 63235.08 71238.18
## 322 323 324 325 326 327 328 329
## 73626.99 99124.51 97501.17 73316.31 72113.41 70469.71 62416.42 59536.85
## 330 331 332 333 334 335 336 337
## 29466.49 33156.36 33596.16 35917.54 35257.97 41028.59 42104.86 37349.46
## 338 339 340 341 342 343 344 345
## 36563.38 36690.13 32006.15 38008.91 38672.74 38931.35 39807.70 41594.76
## 346 347 348 349 350 351 352 353
## 43417.90 43169.40 36109.60 26671.46 32019.52 30879.80 30469.59 28084.52
## 354 355 356 357 358 359 360 361
## 32766.38 36523.05 40989.50 39177.35 40438.44 42578.26 49978.22 50529.15
## 362 363 364 365 366 367 368 369
## 50730.95 53195.69 50677.50 50121.69 42762.19 39957.18 36131.70 33908.96
## 370 371 372 373 374 375 376 377
## 29964.23 37256.80 39545.22 47435.81 41403.58 40850.39 39386.76 38919.23
## 378 379 380 381 382 383 384 385
## 29781.05 34382.11 27429.32 35653.94 45993.65 49561.39 47833.65 49826.62
## 386 387 388 389 390 391 392 393
## 56050.78 65542.36 58749.28 53213.92 52942.45 60326.80 60850.51 69524.80
## 394 395 396 397 398 399 400 401
## 58623.41 60191.31 59751.13 59234.66 57720.58 56481.34 43231.34 51822.59
## 402 403 404 405 406 407 408 409
## 50822.36 49787.91 56193.38 48730.99 48041.69 46367.27 42041.68 40871.62
## 410 411 412 413 414 415 416 417
## 38934.22 33024.49 40902.46 43830.73 38499.54 33584.55 48466.01 52313.34
## 418 419 420 421 422 423 424 425
## 56245.20 48709.38 45030.26 43705.36 47294.20 35705.63 35434.66 29689.64
## 426 427 428 429 430 431 432 433
## 35283.19 43616.32 50513.05 47276.34 44343.08 41266.22 41154.33 37629.80
## 434 435 436 437 438 439 440 441
## 33765.61 30997.81 32574.95 34425.56 32428.85 37298.26 43507.93 40258.12
## 442 443 444 445 446 447 448 449
## 39963.92 42972.35 40857.01 44849.19 40092.91 31119.18 29955.87 41320.41
## 450 451 452 453 454 455 456 457
## 41001.74 46656.73 42289.92 42639.82 44261.42 47982.12 37849.57 42702.13
## 458 459 460 461 462 463 464 465
## 38122.30 45696.45 49218.86 51841.19 48568.53 50910.89 51112.56 52860.78
## 466 467 468 469 470 471 472 473
## 52358.04 55303.79 52630.26 57684.86 50940.24 48549.55 47134.74 43756.81
## 474 475 476 477 478 479 480 481
## 47515.48 54983.87 49407.05 51111.44 45896.75 44274.65 47109.51 36516.56
## 482 483 484 485 486 487 488 489
## 30073.75 31944.34 34642.84 36133.82 37100.34 30736.82 43275.55 49940.25
## 490 491 492 493 494 495 496 497
## 56775.16 51483.06 56319.38 63958.22 67772.21 54019.22 44590.71 42614.34
## 498 499 500 501 502 503 504 505
## 42937.76 43729.46 38212.45 40613.01 45918.10 51603.56 52313.71 52424.35
## 506 507 508 509 510 511 512 513
## 46122.02 47462.90 43719.59 46476.91 46142.37 39853.91 40985.75 40160.34
## 514 515 516 517 518 519 520 521
## 41266.72 43908.53 36743.83 32020.01 55925.78 64091.96 67747.36 61121.99
## 522 523 524 525 526 527 528 529
## 62462.37 76057.14 83038.53 57971.95 52838.55 49530.67 53912.58 53418.59
## 530 531 532 533 534 535 536 537
## 43595.59 48590.75 61259.72 55776.93 59176.84 63167.28 60217.91 55245.35
## 538 539 540 541 542 543 544 545
## 48703.26 47358.26 55300.67 55050.92 47606.01 49831.12 49657.67 50351.96
## 546 547 548 549 550 551 552 553
## 40994.74 32825.07 37169.77 45291.74 45088.57 46795.58 40801.44 49820.38
## 554 555 556 557 558 559 560 561
## 50976.45 40748.87 50296.14 58164.12 57528.65 61128.73 56893.17 68661.43
## 562 563 564 565 566 567 568 569
## 85361.04 75445.52 64001.55 68423.67 66546.83 67734.25 59298.72 43277.29
## 570 571 572 573 574 575 576 577
## 50290.52 56199.50 57383.05 59459.93 60107.39 57231.95 69486.59 58853.05
## 578 579 580 581 582 583 584 585
## 52571.95 60178.45 61683.41 54772.95 61043.57 56616.83 53659.46 67238.38
## 586 587 588 589 590 591 592 593
## 52657.49 60006.50 59098.30 52816.45 52121.16 52397.38 43107.22 45905.87
## 594 595 596 597 598 599 600 601
## 40528.84 44777.39 53546.33 46873.25 52736.03 55115.36 60788.08 56943.74
## 602 603 604 605 606 607 608 609
## 61760.58 53323.81 55205.01 55981.85 58291.49 58865.91 58406.38 52580.06
## 610 611 612 613 614 615 616 617
## 59646.24 57705.09 54800.43 51504.41 44462.84 55980.47 59870.39 50800.00
## 618 619 620 621 622 623 624 625
## 61208.65 65396.26 58881.89 81123.60 66236.90 58561.34 60565.55 55915.92
## 626 627 628 629 630 631 632 633
## 46245.14 56959.35 37503.30 37363.44 46937.81 57427.13 55470.49 84217.70
## 634 635 636 637 638 639 640 641
## 74397.71 76599.84 78238.17 72961.17 65675.85 62308.03 50205.36 48572.65
## 642 643 644 645 646 647 648 649
## 47463.53 45944.83 44328.47 47006.99 51627.29 66607.52 81040.81 78162.37
## 650 651 652 653 654 655 656 657
## 79066.70 84941.84 98394.88 93150.22 63401.79 60852.01 57804.49 58789.74
## 658 659 660 661 662 663 664 665
## 55228.49 45635.27 48021.08 52341.56 51533.26 63102.22 62979.84 63269.55
## 666 667 668 669 670 671 672 673
## 51717.82 53078.18 54097.27 49433.15 43409.16 46445.94 44043.26 47532.21
## 674 675 676 677 678 679 680 681
## 45283.25 38116.43 32771.12 32768.63 35519.32 40254.25 42516.07 40519.35
## 682 683 684 685 686 687 688 689
## 40542.95 40992.00 35330.89 41664.19 41161.32 41460.77 43457.49 54171.82
## 690 691 692 693 694 695 696 697
## 62634.32 70688.49 59978.78 55984.10 52864.52 58021.77 48308.67 42003.84
## 698 699 700 701 702 703 704 705
## 35895.82 32612.91 31091.96 36601.97 34864.49 35537.56 41474.13 70151.16
## 706 707 708 709 710 711 712 713
## 76313.51 93871.36 90188.37 92785.22 107819.53 106659.46 84007.54 84355.06
## 714 715 716 717 718 719 720 721
## 75688.14 72789.10 70764.41 53483.64 48879.21 52373.40 49876.69 38985.29
## 722 723 724 725 726 727 728 729
## 44556.62 40825.67 43072.13 40946.04 31647.77 35600.87 36226.48 29858.22
## 730 731 732 733 734 735 736 737
## 47936.67 50185.63 48218.38 53875.62 46277.74 46552.46 54538.57 40982.13
## 738 739 740 741 742 743 744 745
## 37392.04 45898.25 42670.91 44174.38 46944.68 46057.34 42474.36 49467.24
## 746 747 748 749 750 751 752 753
## 44485.44 65459.82 70783.14 66890.23 58820.96 78611.29 71681.60 70600.20
## 754 755 756 757 758 759 760 761
## 55829.00 104576.83 121657.05 126248.62 107764.08 110193.73 109440.75 107468.88
## 762 763 764 765 766 767 768 769
## 60120.01 45162.62 47072.93 45696.70 43672.14 152304.57 156743.92 181962.35
## 770 771 772 773 774 775 776 777
## 185530.84 179465.15 177461.44 184347.42 81507.96 72091.93 38447.96 41882.84
## 778 779 780 781 782 783 784 785
## 42292.05 46692.19 41087.90 41408.07 41250.61 45806.72 40541.08 40238.39
## 786 787 788 789 790 791 792 793
## 45355.18 48381.59 46635.12 43982.95 46723.16 50092.60 50498.69 45039.37
## 794 795 796 797 798 799 800 801
## 41073.16 41658.44 42068.78 36698.37 36780.16 36052.41 35943.02 47551.56
## 802 803 804 805 806 807 808 809
## 50282.28 56898.41 59048.10 53609.39 60775.46 68513.58 57377.93 50448.24
## 810 811 812 813 814 815 816 817
## 44297.13 48109.25 52479.79 50649.53 38652.26 36955.86 44537.88 52892.87
## 818 819 820 821 822 823 824 825
## 44539.38 38920.11 32622.78 43806.13 43984.70 41388.34 35557.16 44727.94
## 826 827 828 829 830 831 832 833
## 52776.99 57242.69 54084.41 53413.84 55977.98 59771.61 60425.82 53726.27
## 834 835 836 837
## 55548.54 54966.88 57213.34 60388.98
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8105
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 5.155973 0.7945398 3.985569
## t2* 2692.639615 165.6603410 888.748928
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 1.13588 5.129756 13.52639
## 2 lag_depvar 1638.40022 2727.076226 4518.65163Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03")))
# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
Data = as.numeric(mstl_data_autplt[, "Data"]),
Trend = as.numeric(mstl_data_autplt[, "Trend"]),
Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)
# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")
# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
facet_wrap(~ Component, scales = "free_y", ncol = 1) +
theme(strip.text = element_text(size = 12),
axis.text.x = element_text(angle = 90, hjust = 1))
library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Apr 28 00:55:23 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Apr 28 00:55:33 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Apr 28 00:55:43 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Apr 28 00:55:52 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Apr 28 00:56:02 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Apr 28 00:56:12 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Apr 28 00:56:22 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Apr 28 00:56:31 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Apr 28 00:56:41 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Apr 28 00:56:51 2025
## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_25<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2025",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_24<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2024",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2020",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_25 %>%
dplyr::right_join(fit_month_gasto_24,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2025","2024","2023","2022","2021","2020"))| Item | 2025 | 2024 | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|---|---|
| Agua | 6.520667 | 6.993667 | 5.195333 | 5.410333 | 5.849167 | 9.93775 |
| Comida | 316.617667 | 326.890000 | 366.009167 | 312.386750 | 317.896583 | 392.93367 |
| Comunicaciones | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electricidad | 57.448667 | 83.582750 | 38.104750 | 47.072333 | 29.523000 | 20.60458 |
| Enceres | 4.396667 | 23.989000 | 18.259750 | 24.219750 | 14.801167 | 39.01200 |
| Farmacia | 0.000000 | 0.000000 | 10.704083 | 2.835000 | 13.996083 | 14.03675 |
| Gas/Bencina | 27.483333 | 44.292667 | 42.636000 | 45.575000 | 13.583667 | 17.25833 |
| Diosi | 30.833000 | 33.319583 | 55.804250 | 31.180667 | 52.687833 | 37.12133 |
| donaciones/regalos | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electrodomésticos/ Mantención casa | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| VTR | 14.663333 | 18.326667 | 12.829167 | 25.156667 | 19.086917 | 19.11375 |
| Netflix | 0.000000 | 1.391417 | 8.713833 | 7.151583 | 7.028750 | 8.24725 |
| Otros | 0.000000 | 76.164000 | 5.481667 | 5.000000 | 0.000000 | 0.00000 |
| Total | 457.963333 | 614.949750 | 563.738000 | 505.988083 | 474.453167 | 558.26542 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table")
tryCatch(uf25 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2025.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf25 <-uf25[[length(uf25)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf25 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf23[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2025, uf25)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 54 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:2685, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)# Configurar API Key
nixtlar::nixtla_set_api_key(Sys.getenv("API_NIXTLA"))## API key has been set for the current session.try(nixtlar::nixtla_set_api_key(Sys.getenv("NIXTLA")))## API key has been set for the current session.# Preparar datos en formato requerido por TimeGPT
uf_timegpt <- uf_serie_corrected %>%
dplyr::rename(ds = date3, y = value) %>%
dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
dplyr::mutate(unique_id = "serie_1")%>%
dplyr::select(unique_id, ds, y)
# Realizar pronóstico con TimeGPT
timegpt_fcst <- nixtlar::nixtla_client_forecast(
uf_timegpt,
h = 298, # 298 días a pronosticar
freq = "D", # Frecuencia diaria
add_history = TRUE, # Incluir datos históricos en el output
level = c(80,95),
model= "timegpt-1-long-horizon",
clean_ex_first = TRUE
)## The specified horizon h exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.# 1. Convertir 'ds' a fecha en ambas tablas
uf_timegpt <- uf_timegpt %>%
mutate(ds = as.Date(ds))
timegpt_fcst <- timegpt_fcst %>%
mutate(ds = as.Date(ds))
# 2. Combinar los datos históricos y el pronóstico
full_data <- bind_rows(
uf_timegpt %>% mutate(type = "Histórico"),
timegpt_fcst %>% mutate(type = "Pronóstico")
)
# Visualizar resultados
ggplot(full_data, aes(x = ds, y = TimeGPT)) +
# Intervalo de confianza del 95%
geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
fill = "#4B9CD3", alpha = 0.2) +
# Intervalo de confianza del 80%
geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
fill = "#4B9CD3", alpha = 0.3) +
# Línea histórica
geom_line(data = filter(full_data, type == "Histórico"),
aes(color = "Histórico"), size = 1) +
# Línea de pronóstico
geom_line(data = filter(full_data, type == "Pronóstico"),
aes(color = "Pronóstico"), size = 1) +
# Línea vertical separadora
geom_vline(xintercept = max(filter(full_data, type == "Histórico")$ds),
linetype = "dashed", color = "red", size = 0.8) +
# Configuración del eje x
scale_x_date(
date_breaks = "3 months", # Reduce la frecuencia de las etiquetas
date_labels = "%b %Y", # Formato de etiquetas (mes y año)
) +
# Configuración del eje y
scale_y_continuous(labels = function(x) format(x, scientific = FALSE)) +
# Configuración de colores
scale_color_manual(
name = "Leyenda",
values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
) +
# Títulos y subtítulos
labs(
title = "Pronóstico de Serie Temporal con TimeGPT",
subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
x = "Fecha",
y = "Valor",
color = "Leyenda"
) +
# Tema y estilos
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, size = 8),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)## Warning: Removed 2685 rows containing missing values or values outside the scale range
## (`geom_line()`).
library(prophet)## Warning: package 'prophet' was built under R version 4.4.3## Loading required package: Rcpp## Warning: package 'Rcpp' was built under R version 4.4.3## Loading required package: rlang## Warning: package 'rlang' was built under R version 4.4.3##
## Attaching package: 'rlang'## The following objects are masked from 'package:purrr':
##
## %@%, flatten, flatten_chr, flatten_dbl, flatten_int, flatten_lgl,
## flatten_raw, invoke, splice## The following object is masked from 'package:sparklyr':
##
## invoke## The following object is masked from 'package:data.table':
##
## := model <- prophet(
cbind.data.frame(ds= as.Date(uf_timegpt$ds), y=uf_timegpt$y),
# Trend flexibility
growth = "linear",
changepoint.prior.scale = 0.05, # Reduced for smoother trend
n.changepoints = 50, # Increased from default 25
# Seasonality
yearly.seasonality = TRUE,
weekly.seasonality = TRUE,
daily.seasonality = FALSE, # Disabled for daily data
seasonality.mode = "additive",
seasonality.prior.scale = 15, # Increased to capture stronger seasonality
# Holidays (if applicable)
# holidays = generated_holidays # Create with add_country_holidays()
# Uncertainty intervals
interval.width = 0.95,
uncertainty.samples = 1000
)
future <- make_future_dataframe(model, periods = 298, include_history = T)
forecast <- predict(model, future)
forecast <- forecast[, c("ds", "yhat", "yhat_lower", "yhat_upper")]
forecast$pred <- ifelse(forecast$ds > max(uf_timegpt$ds), 1,0)## Warning in check_tzones(e1, e2): 'tzone' attributes are inconsistentforecast$ds <- as.Date(forecast$ds)
ggplot(forecast, aes(x = ds, y = yhat)) +
geom_ribbon(aes(ymin = yhat_lower, ymax = yhat_upper),
fill = "#9ecae1", alpha = 0.4) +
geom_line(color = "#08519c", linewidth = 0.8) +
geom_vline(xintercept = max(uf_timegpt$ds), color = "red", linetype = "dashed", linewidth=1) +
scale_x_date(date_breaks = "6 months", date_labels = "%y %b") +
scale_y_continuous(labels = scales::comma) +
labs(title = "Valores predichos (95%IC)",
# subtitle = "March 10, 2025 - May 7, 2025",
x = "Fecha",
y = "Valor",
# caption = "Source: Prophet Forecast Model"
) +
theme_minimal() +
theme(
plot.title = element_text(face = "bold", size = 14),
plot.subtitle = element_text(color = "gray50"),
axis.text.x = element_text(angle = 45, hjust = 1),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
plot.caption = element_text(color = "gray30")
)
La proyección de la UF a 298 días más 2025-05-09 00:04:58 sería de: 26.714 pesos// Percentil 95% más alto proyectado: 35.134,38
Según TimeGPT: La proyección de la UF a 298 días más 2026-03-03 sería de: 40.071,14 pesos// Percentil 80% más alto proyectado: 40.455,08 pesos// Percentil 95% más alto proyectado: 41.529,12
Según prophet: La proyección de la UF a 298 días más 2026-03-03 sería de: 40.248 pesos// Percentil 95% más alto proyectado: 44.526
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 26328.94 | 26324.01 |
| Lo.80 | 26461.63 | 26488.42 |
| Point.Forecast | 26714.13 | 26799.01 |
| Hi.80 | 31516.13 | 32160.60 |
| Hi.95 | 34398.32 | 34998.86 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(1,1,1)
##
## Coefficients:
## ar1 ma1
## 0.3483 -0.9346
## s.e. 0.1374 0.0704
##
## sigma^2 = 38816: log likelihood = -488.95
## AIC=983.91 AICc=984.26 BIC=990.78paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 intercept xreg
## 0.3520 564.4043 14.9274
## s.e. 0.1125 301.6200 9.2576
##
## sigma^2 = 37056: log likelihood = -492.78
## AIC=993.57 AICc=994.15 BIC=1002.78forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 674.8956 | 656.9731 | 697.1199 |
| Lo.80 | 814.4207 | 806.2363 | 793.4172 |
| Point.Forecast | 1077.9901 | 1089.9752 | 1012.6503 |
| Hi.80 | 1341.5594 | 1379.9564 | 1291.7041 |
| Hi.95 | 1481.0846 | 1533.4632 | 1468.9823 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Gastos_casa_mensual_2022$mes_ano <- gsub("marzo", "Mar", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("abril", "Apr", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("mayo", "May", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("junio", "Jun", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("julio", "Jul", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("agosto", "Aug", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("septiembre", "Sep", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("octubre", "Oct", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("noviembre", "Nov", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("diciembre", "Dec", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("enero", "Jan", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("febrero", "Feb", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022<- dplyr::filter(Gastos_casa_mensual_2022, !is.na(Tami))
Gastos_casa_mensual_2022$mes_ano <- parse_date_time(Gastos_casa_mensual_2022$mes_ano, "%b_%Y")
Gastos_casa_mensual_2022$mes_ano <- as.Date(as.character(Gastos_casa_mensual_2022$mes_ano))
Gastos_casa_mensual_2022_timegpt <- Gastos_casa_mensual_2022 %>%
mutate(value = Tami + Andrés) %>%
rename(ds = mes_ano, y = value) %>%
mutate(#ds= format(ds, "%Y-%m"),
unique_id = "1") %>% #it is only one series
select(unique_id, ds, y)
#Convertir la base de UF a mensual
uf_timegpt_my <- uf_serie_corrected %>%
dplyr::rename(ds = date3, y = value) %>%
dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
dplyr::mutate(unique_id = "serie_1")%>%
dplyr::select(unique_id, ds, y) %>%
mutate(ds = ymd(ds)) %>% # Convert 'ds' to Date
mutate(month = month(ds), year = year(ds)) %>% # Extract month and year
group_by(month, year) %>% # Group by month and year
summarise(average_y = mean(y))%>% # Calculate average y
mutate(ds = as.Date(paste0(year,"-",month, "-01")))%>%
ungroup()%>%
select(ds, uf=average_y)
Gastos_casa_mensual_2022_timegpt_ex<-
Gastos_casa_mensual_2022_timegpt |>
dplyr::left_join(uf_timegpt_my, by=c("ds"="ds"))
#Historical Exogenous Variables: These should be included in the input data immediately following the id_col, ds, and y columns
gastos_timegpt_fcst <- nixtlar::nixtla_client_forecast(
Gastos_casa_mensual_2022_timegpt_ex,
h = 12,
freq = "M", # Monthly frequency
add_history = TRUE,
level = c(80, 95),
model = "timegpt-1",#"timegpt-1-long-horizon",
clean_ex_first = TRUE
)
# Convert 'ds' to Date format in both tables
Gastos_casa_mensual_2022_timegpt_corr <- Gastos_casa_mensual_2022_timegpt %>%
mutate(ds = as.Date(paste0(ds, "-01"))) # Add day to make it a complete date
gastos_timegpt_fcst <- gastos_timegpt_fcst %>%
mutate(ds = as.Date(paste0(ds, "-01"))) # Add day to make it a complete date
# Combine historical and forecast data
full_data_gastos <- bind_rows(
Gastos_casa_mensual_2022_timegpt_corr %>% mutate(type = "Histórico"),
gastos_timegpt_fcst %>% mutate(type = "Pronóstico")
)
full_data_gastos |>
dplyr::mutate(y= ifelse(is.na(y),TimeGPT, y)) |>
# Visualize results
ggplot(aes(x = ds, y = y)) +
geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
fill = "#4B9CD3", alpha = 0.2) +
geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
fill = "#4B9CD3", alpha = 0.3) +
geom_line(aes(color = type), linewidth = 1.5) +
geom_vline(xintercept = max(filter(full_data_gastos, type == "Histórico")$ds),
linetype = "dashed", color = "red", linewidth = 0.8) +
scale_x_date(
date_breaks = "3 months",
date_labels = "%b %Y"
) +
scale_y_continuous(
name = "Gastos Totales",
labels = scales::comma,
breaks = pretty(full_data_gastos$y, n = 10),
expand = expansion(mult = c(0.05, 0.05))
) +
scale_color_manual(
name = "Leyenda",
values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
) +
labs(
title = "Pronóstico de Gastos Mensuales (TimeGPT, ajustando por UF promedio mensual)",
subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
x = "Fecha",
y = "Gastos Totales",
color = "Leyenda"
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.4.0 (2024-04-24 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows Server 2022 x64 (build 20348)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
## system code page: 65001
##
## time zone: UTC
## tzcode source: internal
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] prophet_1.0 rlang_1.1.6 Rcpp_1.0.14
## [4] CausalImpact_1.3.0 bsts_0.9.10 BoomSpikeSlab_1.2.6
## [7] Boom_0.9.15 scales_1.4.0 ggiraph_0.8.13
## [10] tidytext_0.4.2 DT_0.33 autoplotly_0.1.4
## [13] rvest_1.0.4 plotly_4.10.4 xts_0.14.1
## [16] forecast_8.24.0 wordcloud_2.6 RColorBrewer_1.1-3
## [19] SnowballC_0.7.1 tm_0.7-16 NLP_0.3-2
## [22] tsibble_1.1.6 lubridate_1.9.4 forcats_1.0.0
## [25] dplyr_1.1.4 purrr_1.0.4 tidyr_1.3.1
## [28] tibble_3.2.1 ggplot2_3.5.2 tidyverse_2.0.0
## [31] sjPlot_2.8.17 lattice_0.22-6 gridExtra_2.3
## [34] plotrix_3.8-4 sparklyr_1.9.0 httr_1.4.7
## [37] readxl_1.4.5 zoo_1.8-14 stringr_1.5.1
## [40] stringi_1.8.7 DataExplorer_0.8.3 data.table_1.17.0
## [43] reshape2_1.4.4 fUnitRoots_4040.81 plyr_1.8.9
## [46] readr_2.1.5
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-9 cellranger_1.1.0 datawizard_1.0.2
## [4] janitor_2.2.1 lifecycle_1.0.4 httr2_1.1.2
## [7] StanHeaders_2.32.10 globals_0.17.0 vroom_1.6.5
## [10] MASS_7.3-60.2 insight_1.2.0 crosstalk_1.2.1
## [13] magrittr_2.0.3 sass_0.4.10 rmarkdown_2.29
## [16] jquerylib_0.1.4 yaml_2.3.10 fracdiff_1.5-3
## [19] askpass_1.2.1 pkgbuild_1.4.7 DBI_1.2.3
## [22] abind_1.4-8 quadprog_1.5-8 nnet_7.3-19
## [25] rappdirs_0.3.3 inline_0.3.21 data.tree_1.1.0
## [28] tokenizers_0.3.0 listenv_0.9.1 anytime_0.3.11
## [31] performance_0.13.0 spatial_7.3-17 parallelly_1.43.0
## [34] codetools_0.2-20 xml2_1.3.8 tidyselect_1.2.1
## [37] ggeffects_2.2.1 farver_2.1.2 urca_1.3-4
## [40] its.analysis_1.6.0 matrixStats_1.5.0 stats4_4.4.0
## [43] jsonlite_2.0.0 ellipsis_0.3.2 Formula_1.2-5
## [46] systemfonts_1.2.2 tools_4.4.0 glue_1.8.0
## [49] xfun_0.52 TTR_0.24.4 ggfortify_0.4.17
## [52] loo_2.8.0 withr_3.0.2 timeSeries_4041.111
## [55] fastmap_1.2.0 boot_1.3-30 openssl_2.3.2
## [58] caTools_1.18.3 digest_0.6.37 timechange_0.3.0
## [61] R6_2.6.1 colorspace_2.1-1 networkD3_0.4.1
## [64] gtools_3.9.5 generics_0.1.3 htmlwidgets_1.6.4
## [67] pkgconfig_2.0.3 gtable_0.3.6 timeDate_4041.110
## [70] lmtest_0.9-40 selectr_0.4-2 janeaustenr_1.0.0
## [73] htmltools_0.5.8.1 carData_3.0-5 tseries_0.10-58
## [76] snakecase_0.11.1 knitr_1.50 rstudioapi_0.17.1
## [79] tzdb_0.5.0 uuid_1.2-1 nlme_3.1-164
## [82] curl_6.2.2 cachem_1.1.0 sjlabelled_1.2.0
## [85] KernSmooth_2.23-22 parallel_4.4.0 fBasics_4041.97
## [88] pillar_1.10.2 vctrs_0.6.5 gplots_3.2.0
## [91] slam_0.1-55 car_3.1-3 dbplyr_2.5.0
## [94] evaluate_1.0.3 cli_3.6.5 compiler_4.4.0
## [97] crayon_1.5.3 future.apply_1.11.3 labeling_0.4.3
## [100] sjmisc_2.8.10 rstan_2.32.7 QuickJSR_1.7.0
## [103] viridisLite_0.4.2 assertthat_0.2.1 lazyeval_0.2.2
## [106] Matrix_1.7-0 sjstats_0.19.0 hms_1.1.3
## [109] bit64_4.6.0-1 future_1.40.0 nixtlar_0.6.2
## [112] extraDistr_1.10.0 igraph_2.1.4 RcppParallel_5.1.10
## [115] bslib_0.9.0 quantmod_0.4.27 bit_4.6.0#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))